Rompiendo el silencio

After the “root cause analysis”, factor prioritization and response target definition are done, it is necessary to review these values with a focus on “good project management practice”.

Therefore, it has to be considered that doing a research on all desired functions will take time and will consume money and resources. In most projects, there is the sword of Damocles over the project team, which means that there is always not enough time, money and resources. In this context, one often hears a contradiction in terms such as, "We do not have the time for experiments “. This interaction is visualized in Fig. 10.

To briefly illustrate, it can be assumed that the functions to be examined need too much time. These circumstances can only be compensated by moving the timeline or tapping additional resources. Both options will impact the budget. Thus, it is always important to check at regular intervals if any (planned) actions are still result-related and necessary.

Fig. 10. Interaction between the main components of project management.

3. Screening

The challenge for machine operators is to get the run-in process done as quickly as possible.

This means a minimum number of experiments with a maximum ability to describe cause and effect. The operators should be sensitized to the fact that small adjustments in the setup of the machine can have a great impact on the quality of the injection molding parts.

Therefore, a well-structured approach and high quality data are needed. To reach these goals, the method of “Design of Experiments” will be introduced on the basis of the software “Modde”⁵. This is done because further steps “optimization” and the “Design Space Estimation” are incorporated tools within the software “Modde”.

In general, the software is used when there is a lack of knowledge how cause and effect are related.

The use of "Design of Experiments" is an admission that the correlation of factors and effect could not be fully captured. This condition is depicted as a black-box (Fig. 11). By varying the factors within and according to a structured design, a regression model can be derived.

From this model, the effect of the factors can be calculated. Since the experiments and thus the factors are varied to an estimated optimal range, some of the results are, of course, likely to deviate from the optimal targets. Nevertheless, all experiments and their results are very

5Modde is a software product of Umetrics, a company of MKS Instruments Inc.

Fig. 11. Process Black-Box.

important because the basis of an entire image of it can be mapped on the work-space. The screening-process starts to extract the most influential factors from the familiarization process. These factors will be used to start with the Design of Experiments method. But not necessarily all factors must be examined in light of variability. So, some of these less important factors should be frozen at a certain level, which still ensures a good product quality. This is because the number of factors substantially affects the sum of the experiments; the number of evaluation criteria (responses) is of secondary importance. In the best case, the factors are quantitative, and so simple geometric designs can be generated.

It is more difficult when they are qualitative, e. g. “machine 1” or “machine 2”. Such qualitative or attributive parameters increase the number of experiments because they hamper the generation of the design. Once the factors have been identified, it is necessary to assess their effect. The effect is that which is exerted on the target variable when the factor is varied from its minimum to its maximum setting. Since all the factors are changed simultaneously in a factorial design, this effect is difficult to estimate.

It is therefore useful to debate and determine the factor variations within a group of experienced staff. Some factors are even trickier to formulate than the qualitative factors, such as temperature or pressure profiles. Just as in the machine, in the factorial experiment, the profiles can be programmed with some nodes such as (initial value + 9 nodes). The start and end values of the profile are known. Moreover, the process specifies a sloping curve (Fig 12, 13). If the profiles were programmed with real numbers, the sloping profile would necessitate the use of a great many programmed extra constraints. These factor restrictions limit the choice of experimental models and greatly increase the number of necessary experiments. For this reason, a mathematical formulation of the profiles is recommended which allows restrictions to be dispensed with entirely. Thus, the pressure profile is calculated, for instance, from the given initial value and the maximum decrease in pressure (in bar) per node:

Δp initial value max. end value min.

number of nodes 1

The following variation thus

arises for each node in (2): (1)

Node (i +1) = node (i) – (min. 0, max. Δp) (2)

Another way to represent the profile is the use of a simple two point (FU 3).

value of factor setting mx m = Cf. (4) ; x = node of factor profile;

b0 = bias ; (3)

Fig. 12. Injection profile ².

Fig. 13. Hold pressure ².

For this, the initial value and the maximum slope (FU 4) of “initial valuemax“ and “end valuemin” is required. From this data the increasing/decreasing constant slope/node can be described with two factors instead of several nodes (Fig.14, 15). These factors are the “start-value” and the constant amount to increase/ decrease per node, both must have a min./max. variation. Therefore, a constraint (FU 4) needs to be defined that, if decreasing, or increasing with a bigger constant amount beginning from a varied start level cannot lead to exceeding the final max. or min. final-profile-levels.

m initial value end value

2 Δp = const. for each node, note (5) (4)

Node (i +1) = node (i) – Δp Value node Value (5)

Fig. 14. Injection profile ².

Fig. 15. Hold pressure ².

Considering that the individual nodes from the first approach can be quantitatively described independently of each other, the experimental scope and so the following experiments are extremely reduced due to freezing of these node factors at a promising level or due to explaining them with the second formula approach (constant amount/node) and smaller variance space. The formulation with the second approach (slope) is also a good approach to describe the constraints with a very limited number of experiments, but not as independent and individual an approach as the first described approach. In the most circumstances, the more effective second approach is recommended. The profile of the injection values could be formulated in the same manner.